A Hybrid Multiobjective Particle Swarm Optimization Approach for Non-redundant Gene Marker Selection
The gene markers or biological markers indicate change in expression or state of protein that correlates with the risk or progression of a disease, or with the susceptibility of the disease to a given treatment. There are many approaches for detecting these informative genes from high dimensional microarray data. But in practice, for most of the cases a set of redundant marker genes are identified. Motivated by this fact a hybrid multiobjective optimization method has been proposed which can find small set of non-redundant disease related genes. In this article the optimization problem has been modeled as multiobjective problem which is based on the framework of particle swarm optimization. As the wrapper approaches depend on a specific classifier evaluation, hence artificial neural network classifier is used as evaluation criteria. Using the real life datasets, performance of proposed algorithm has been compared with other different techniques.
KeywordsMultiobjective optimization Particle swarm optimization Biomarker Non-redundant Artificial neural network.
Unable to display preview. Download preview PDF.
- K. Deb, A. Pratap, S. Agrawal, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6:182–197, 2002.Google Scholar
- K. Deb. Multi-objective optimization using evolutionary algorithms. England: John Wiley and Sons, Ltd., 2001.Google Scholar
- X. Cui and T. E. Potok. Document clustering using particle swarm optimization. IEEE Swarm Intelligence Symposium, Pasadena, California, 2005.Google Scholar
- X. Cui and T. E. Potok. Document clustering analysis based on hybrid pso+k-means algorithm. Journal of Computer Sciences, Special Issue:27–33, 2005.Google Scholar
- K. E. Parsopoulos. Particle swarm optimization and intelligence: Advances and applications. Information science reference, Hershey, New York, 2010.Google Scholar
- M. R. Sierra and C. A. Coello Coello. Multi-objective particle swarm optimizers: A survey of the state-of-the-art. International Journal of Computational Intelligence Research, 2(3):287–308, 2006.Google Scholar
- M. H. Cheok, W. Yang, C-H. Pui, J. R. Downing, C. Cheng, C. W. Naeve, M. V. Relling, and W. E. Evans. Characterization of pareto dominance. Operations Research Letters, 31, Issue 1:711, 2003.Google Scholar
- U. Maulik, A. Mukhopadhyay, and S. Bandyopadhyay. Combining pareto-optimal clusters using supervised learning for identifying co-expressed genes. BMC Bioinformatics, 10(27), 2009.Google Scholar
- C. A. Coello Coello. A comprehensive survey of evolutionary-based multiobjective optimization techniques. Knowledge and Information Systems, 1(3):129–156, 1999.Google Scholar
- W. McCulloch and W. Pitts. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5:115133, 1943.Google Scholar
- D. Rumelhart and J. McClelland. Parallel Distributed Processing. MIT Press, Cambridge, Mass. Google Scholar
- X. Wang and O. Gotoh. Accurate molecular classification of cancer using simple rules. BMC Medical Genomics, 2009.Google Scholar
- J. Li, H. Liu, S-K. Ng, and L. Wong. Discovery of significant rules for classifying cancer diagnosis data. Bioinformatics, 19, 2003.Google Scholar
- M. A. Shipp, K. N. Ross, P. Tamayo, A. P. Weng, J. L. Kutok, R.C.T. Aguiar, M. Gaasenbee, M. Angelo, M. Reich, G. S. Pinkus, T. S. Ray, M. A. Koval, K. W. Last, A. Norton, T. A. Lister, J. Mesirov, D. S. Neuberg, E. S. Lander, J. C. Aster, and T. R. Golub. Diffuse large b-cell lymphoma outcome prediction by geneexpression profiling and supervised machine learning. Nature Medicine, 8, 2002.Google Scholar
- J-S. Zhang, Q. Liu, Y-M. Li, S. H. Hall, F. S. French, and Y-L. Zhang. Genome-wide profiling of segmental-regulated transcriptomes in human epididymis using oligo microarray. Molecular and Cellular Endocrinology, 2006.Google Scholar